AIT Deep Learning Pedestrian Detection Project¶
Note: We used GitHub Copilot to generate some of the code, such as displaying images using matplotlib.
References:
In [1]:
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
In [2]:
import gc
import json
import random
import keras_cv
import tensorflow as tf
import matplotlib.pyplot as plt
from keras import backend as K
from keras_cv import bounding_box, visualization
from tensorflow import keras
from tensorflow.keras.callbacks import Callback
2024-05-05 23:05:47.321016: I tensorflow/core/util/port.cc:111] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`. 2024-05-05 23:05:47.349023: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered 2024-05-05 23:05:47.349043: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered 2024-05-05 23:05:47.349047: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered 2024-05-05 23:05:47.352942: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. To enable the following instructions: SSE4.1 SSE4.2 AVX AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
Using TensorFlow backend
/home/lehoangchibach/.local/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html from .autonotebook import tqdm as notebook_tqdm
We are running this program on Bach's computer that has GPU.
In [3]:
gpus = tf.config.list_physical_devices("GPU")
tf.config.set_logical_device_configuration(
gpus[0],
[tf.config.LogicalDeviceConfiguration(memory_limit=8000)],
)
logical_gpus = tf.config.list_logical_devices("GPU")
print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
1 Physical GPUs, 1 Logical GPUs
2024-05-05 23:05:50.616105: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 2024-05-05 23:05:50.630245: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 2024-05-05 23:05:50.630459: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 2024-05-05 23:05:50.631715: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 2024-05-05 23:05:50.631893: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 2024-05-05 23:05:50.632013: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 2024-05-05 23:05:50.682097: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 2024-05-05 23:05:50.682295: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 2024-05-05 23:05:50.682420: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 2024-05-05 23:05:50.682529: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1886] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 8000 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:2f:00.0, compute capability: 8.6
1-1 Processing the image and annotation¶
The Pedestrian dataset contains train and validation data. We are using the train data for training and validation, and validation data for testing. Images are stored in images/{train/val}/{image_id}.jpg, and annotations are stored in annotations/dhd_pedestrian_traffic_{train/val}.json.
In [4]:
SPLIT_RATIO = 0.8
BATCH_SIZE = 8
LEARNING_RATE = 0.001
EPOCH = 5
GLOBAL_CLIPNORM = 10.0
In [5]:
""" Returns bounding boxes for every image
"""
def convert_annotations(img_type: str) -> (int, dict):
with open(f"annotations/dhd_pedestrian_traffic_{img_type}.json") as f:
annotations = json.load(f)
boxes = {}
image_ids = []
for anno in annotations["annotations"]:
image_id = anno["image_id"]
if image_id not in image_ids:
image_ids.append(image_id)
if image_id not in boxes:
boxes[image_id] = {}
boxes[image_id]["image_path"] = f"images/{img_type}/{image_id}.jpg"
boxes[image_id]["boxes"] = []
boxes[image_id]["class_ids"] = []
boxes[image_id]["boxes"].append(anno["bbox"])
boxes[image_id]["class_ids"].append(0)
return image_ids, boxes
In [6]:
""" Load image into uint8 tensor
"""
def load_image(image_path):
image = tf.io.read_file(image_path)
image = tf.image.decode_jpeg(image, channels=3)
return image
""" Load image and bounding_boxes pair
"""
def load_dataset(image_path, classes, bbox):
image = load_image(image_path)
bounding_boxes = {
"classes": tf.cast(classes, dtype=tf.float32),
"boxes": bbox,
}
return {"images": tf.cast(image, tf.float32), "bounding_boxes": bounding_boxes}
We are using data augmenter to generate diverse training dataset that does not have to be loaded to the memory at once. For phase 2, we are using random holizontal flip and resizing.
In [7]:
augmenter = keras.Sequential(
layers=[
keras_cv.layers.RandomFlip(mode="horizontal", bounding_box_format="xywh"),
keras_cv.layers.JitteredResize(
target_size=(640, 640), scale_factor=(0.75, 1.3), bounding_box_format="xywh"
),
]
)
2024-05-05 23:05:50.756429: I tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:746] failed to allocate 7.81GiB (8388608000 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory
In [8]:
def create_raw_dataset(image_ids, annos):
image_paths = []
boxes = []
class_ids = []
set_images_ids = set(image_ids)
for i in annos:
if i not in set_images_ids:
continue
x = annos[i]
image_paths.append(x["image_path"])
boxes.append(x["boxes"])
class_ids.append(x["class_ids"])
bbox = tf.ragged.constant(boxes)
classes = tf.ragged.constant(class_ids)
image_paths = tf.ragged.constant(image_paths)
return tf.data.Dataset.from_tensor_slices((image_paths, classes, bbox))
def process_train_valid():
image_ids, annos = convert_annotations("train")
slice_index = int(len(image_ids) * SPLIT_RATIO)
train_image_ids = image_ids[:slice_index]
valid_image_ids = image_ids[slice_index:]
train_image_ids = random.choices(train_image_ids, k=500)
valid_image_ids = random.choices(valid_image_ids, k=50)
train_data = create_raw_dataset(train_image_ids, annos)
valid_data = create_raw_dataset(train_image_ids, annos)
train_ds = train_data.map(load_dataset, num_parallel_calls=tf.data.AUTOTUNE)
train_ds = train_ds.shuffle(BATCH_SIZE * 4)
train_ds = train_ds.ragged_batch(BATCH_SIZE, drop_remainder=True)
train_ds = train_ds.map(augmenter, num_parallel_calls=tf.data.AUTOTUNE)
valid_ds = valid_data.map(load_dataset, num_parallel_calls=tf.data.AUTOTUNE)
# valid_ds = valid_ds.shuffle(BATCH_SIZE * 4)
valid_ds = valid_ds.ragged_batch(BATCH_SIZE, drop_remainder=True)
valid_ds = valid_ds.map(
keras_cv.layers.Resizing(
640, 640, bounding_box_format="xywh", pad_to_aspect_ratio=True
),
num_parallel_calls=tf.data.AUTOTUNE,
)
return train_ds, valid_ds
In [9]:
train_ds, valid_ds = process_train_valid()
class_mapping = {0: 'pedestrian'} # We only have one class for our bounding box, pedestrian.
1-2 Visualizing the Dataset¶
In [10]:
def visualize_dataset(inputs, value_range, rows, cols, bounding_box_format):
inputs = next(iter(inputs.take(1)))
images, bounding_boxes = inputs["images"], inputs["bounding_boxes"]
visualization.plot_bounding_box_gallery(
images,
value_range=value_range,
rows=rows,
cols=cols,
y_true=bounding_boxes,
scale=5,
font_scale=0.7,
bounding_box_format=bounding_box_format,
class_mapping=class_mapping,
)
Augmented Training images¶
In [11]:
visualize_dataset(
train_ds, bounding_box_format="xywh", value_range=(0, 255), rows=2, cols=2
)
2024-05-05 23:06:02.913355: I tensorflow/core/framework/local_rendezvous.cc:421] Local rendezvous recv item cancelled. Key hash: 3278516992175784195
Validation images¶
In [12]:
visualize_dataset(
valid_ds, bounding_box_format="xywh", value_range=(0, 255), rows=2, cols=2
)
2 Building and training the model¶
2-1 Model Creation¶
In [13]:
# We are converting dictionary to tuple as tensorflow needs to have tuple of each element.
def dict_to_tuple(inputs):
return inputs["images"], bounding_box.to_dense(
inputs["bounding_boxes"], max_boxes=32
)
train_ds = train_ds.map(dict_to_tuple, num_parallel_calls=tf.data.AUTOTUNE)
train_ds = train_ds.prefetch(tf.data.AUTOTUNE)
valid_ds = valid_ds.map(dict_to_tuple, num_parallel_calls=tf.data.AUTOTUNE)
valid_ds = valid_ds.prefetch(tf.data.AUTOTUNE)
In [14]:
K.clear_session()
backbone = keras_cv.models.YOLOV8Backbone.from_preset(
# "yolo_v8_s_backbone_coco" # We will use backend yolov8 small backbone with coco weights
"yolo_v8_xs_backbone_coco"
)
yolo = keras_cv.models.YOLOV8Detector(
num_classes=len(class_mapping),
bounding_box_format="xywh",
backbone=backbone,
fpn_depth=1,
)
optimizer = tf.keras.optimizers.Adam(
learning_rate=LEARNING_RATE,
global_clipnorm=GLOBAL_CLIPNORM,
)
yolo.compile(
optimizer=optimizer, classification_loss="binary_crossentropy", box_loss="ciou"
)
/home/lehoangchibach/anaconda3/envs/gpu11_cv/lib/python3.11/site-packages/keras_cv/models/backbones/backbone.py:44: UserWarning: `Model.state_updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically. return id(getattr(self, attr)) not in self._functional_layer_ids /home/lehoangchibach/anaconda3/envs/gpu11_cv/lib/python3.11/site-packages/keras_cv/models/backbones/backbone.py:44: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically. return id(getattr(self, attr)) not in self._functional_layer_ids
In [15]:
class ClearMemory(Callback):
def on_epoch_end(self, epoch, logs=None):
gc.collect()
K.clear_session()
es = tf.keras.callbacks.EarlyStopping(
monitor="val_box_loss",
patience=10,
min_delta=0.001,
restore_best_weights=True,
)
clear_mem = ClearMemory()
lr_scheduler = keras.callbacks.ReduceLROnPlateau(
monitor="val_box_loss",
factor=0.5,
patience=7,
min_lr=1e-7,
verbose=1,
)
coco_metrics_callback = keras_cv.callbacks.PyCOCOCallback(
valid_ds, bounding_box_format="xywh"
)
2-2 Training¶
In [16]:
network_history = yolo.fit(
train_ds,
validation_data=valid_ds,
epochs=500,
callbacks=[es, clear_mem, coco_metrics_callback, lr_scheduler],
verbose=1,
)
Epoch 1/500
2024-05-05 23:06:27.423690: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:442] Loaded cuDNN version 8800 2024-05-05 23:06:35.310512: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7a4bc70dbd40 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2024-05-05 23:06:35.310533: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6 2024-05-05 23:06:35.313622: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable. 2024-05-05 23:06:35.360672: I ./tensorflow/compiler/jit/device_compiler.h:186] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.
61/61 [==============================] - 10s 91ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.03s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 73s 638ms/step - loss: 628.4666 - box_loss: 4.2450 - class_loss: 624.2218 - val_loss: 11885.1240 - val_box_loss: 4.6708 - val_class_loss: 11880.4502 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 0.0010 Epoch 2/500 61/61 [==============================] - 13s 210ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 49s 724ms/step - loss: 166.7645 - box_loss: 4.0106 - class_loss: 162.7540 - val_loss: 762.4223 - val_box_loss: 3.1189 - val_class_loss: 759.3034 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 0.0010 Epoch 3/500 61/61 [==============================] - 13s 209ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 48s 716ms/step - loss: 29.5248 - box_loss: 3.7801 - class_loss: 25.7447 - val_loss: 12.0074 - val_box_loss: 4.4908 - val_class_loss: 7.5166 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 0.0010 Epoch 4/500 61/61 [==============================] - 13s 209ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 48s 717ms/step - loss: 11.1990 - box_loss: 3.6438 - class_loss: 7.5552 - val_loss: 10.2624 - val_box_loss: 4.1418 - val_class_loss: 6.1205 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 0.0010 Epoch 5/500 61/61 [==============================] - 13s 209ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 48s 716ms/step - loss: 8.4697 - box_loss: 3.5905 - class_loss: 4.8792 - val_loss: 9.3587 - val_box_loss: 4.1149 - val_class_loss: 5.2438 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 0.0010 Epoch 6/500 61/61 [==============================] - 13s 208ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 48s 715ms/step - loss: 7.5217 - box_loss: 3.3122 - class_loss: 4.2095 - val_loss: 7.5142 - val_box_loss: 3.7268 - val_class_loss: 3.7874 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 0.0010 Epoch 7/500 61/61 [==============================] - 13s 209ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 48s 714ms/step - loss: 6.8056 - box_loss: 3.0636 - class_loss: 3.7420 - val_loss: 7.2681 - val_box_loss: 3.4981 - val_class_loss: 3.7700 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 0.0010 Epoch 8/500 61/61 [==============================] - 13s 209ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 48s 713ms/step - loss: 6.4397 - box_loss: 2.9154 - class_loss: 3.5243 - val_loss: 6.8105 - val_box_loss: 3.3716 - val_class_loss: 3.4389 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 0.0010 Epoch 9/500 61/61 [==============================] - 13s 208ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Epoch 9: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257. 61/61 [==============================] - 48s 718ms/step - loss: 5.9256 - box_loss: 2.7541 - class_loss: 3.1715 - val_loss: 8.0378 - val_box_loss: 3.4932 - val_class_loss: 4.5446 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 0.0010 Epoch 10/500 61/61 [==============================] - 14s 209ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 49s 724ms/step - loss: 5.5334 - box_loss: 2.6291 - class_loss: 2.9043 - val_loss: 5.7379 - val_box_loss: 3.0138 - val_class_loss: 2.7241 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 5.0000e-04 Epoch 11/500 61/61 [==============================] - 14s 209ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 48s 717ms/step - loss: 5.1945 - box_loss: 2.5052 - class_loss: 2.6893 - val_loss: 5.6435 - val_box_loss: 2.9496 - val_class_loss: 2.6938 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 5.0000e-04 Epoch 12/500 61/61 [==============================] - 14s 209ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 48s 715ms/step - loss: 5.0490 - box_loss: 2.5269 - class_loss: 2.5221 - val_loss: 5.4345 - val_box_loss: 2.8412 - val_class_loss: 2.5934 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 5.0000e-04 Epoch 13/500 61/61 [==============================] - 14s 210ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 49s 729ms/step - loss: 4.7439 - box_loss: 2.3479 - class_loss: 2.3960 - val_loss: 4.9446 - val_box_loss: 2.7395 - val_class_loss: 2.2051 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 5.0000e-04 Epoch 14/500 61/61 [==============================] - 14s 208ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 49s 722ms/step - loss: 4.5920 - box_loss: 2.2884 - class_loss: 2.3035 - val_loss: 5.4185 - val_box_loss: 2.7589 - val_class_loss: 2.6596 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 5.0000e-04 Epoch 15/500 61/61 [==============================] - 14s 209ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 49s 723ms/step - loss: 4.5386 - box_loss: 2.2775 - class_loss: 2.2611 - val_loss: 5.1114 - val_box_loss: 2.7024 - val_class_loss: 2.4090 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 5.0000e-04 Epoch 16/500 61/61 [==============================] - 14s 209ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 49s 725ms/step - loss: 4.5359 - box_loss: 2.3292 - class_loss: 2.2067 - val_loss: 5.0798 - val_box_loss: 2.6538 - val_class_loss: 2.4261 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 5.0000e-04 Epoch 17/500 61/61 [==============================] - 14s 209ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 49s 722ms/step - loss: 4.2178 - box_loss: 2.1993 - class_loss: 2.0185 - val_loss: 4.7877 - val_box_loss: 2.5623 - val_class_loss: 2.2254 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 5.0000e-04 Epoch 18/500 61/61 [==============================] - 14s 209ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 49s 715ms/step - loss: 4.4399 - box_loss: 2.2778 - class_loss: 2.1621 - val_loss: 4.7917 - val_box_loss: 2.6497 - val_class_loss: 2.1420 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 5.0000e-04 Epoch 19/500 61/61 [==============================] - 14s 209ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 49s 726ms/step - loss: 4.2891 - box_loss: 2.1904 - class_loss: 2.0987 - val_loss: 4.6489 - val_box_loss: 2.5539 - val_class_loss: 2.0950 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 5.0000e-04 Epoch 20/500 61/61 [==============================] - 14s 209ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 49s 724ms/step - loss: 4.1031 - box_loss: 2.1262 - class_loss: 1.9769 - val_loss: 4.6009 - val_box_loss: 2.5457 - val_class_loss: 2.0552 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 5.0000e-04 Epoch 21/500 61/61 [==============================] - 14s 209ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 49s 724ms/step - loss: 3.9921 - box_loss: 2.1037 - class_loss: 1.8884 - val_loss: 4.7553 - val_box_loss: 2.4684 - val_class_loss: 2.2869 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 5.0000e-04 Epoch 22/500 61/61 [==============================] - 14s 210ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 48s 722ms/step - loss: 3.9312 - box_loss: 2.0852 - class_loss: 1.8461 - val_loss: 4.3980 - val_box_loss: 2.4309 - val_class_loss: 1.9671 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 5.0000e-04 Epoch 23/500 61/61 [==============================] - 14s 209ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 48s 714ms/step - loss: 3.9937 - box_loss: 2.0982 - class_loss: 1.8955 - val_loss: 4.2008 - val_box_loss: 2.4443 - val_class_loss: 1.7564 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 5.0000e-04 Epoch 24/500 61/61 [==============================] - 14s 210ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 49s 727ms/step - loss: 3.8447 - box_loss: 2.0204 - class_loss: 1.8243 - val_loss: 4.9892 - val_box_loss: 2.4155 - val_class_loss: 2.5737 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 5.0000e-04 Epoch 25/500 61/61 [==============================] - 14s 209ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 49s 722ms/step - loss: 3.7156 - box_loss: 1.9791 - class_loss: 1.7365 - val_loss: 4.0352 - val_box_loss: 2.3187 - val_class_loss: 1.7166 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 5.0000e-04 Epoch 26/500 61/61 [==============================] - 14s 209ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 49s 719ms/step - loss: 3.8379 - box_loss: 2.0300 - class_loss: 1.8079 - val_loss: 4.1802 - val_box_loss: 2.4317 - val_class_loss: 1.7484 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 5.0000e-04 Epoch 27/500 61/61 [==============================] - 14s 209ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 49s 720ms/step - loss: 3.5567 - box_loss: 1.9117 - class_loss: 1.6450 - val_loss: 4.0962 - val_box_loss: 2.3909 - val_class_loss: 1.7053 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 5.0000e-04 Epoch 28/500 61/61 [==============================] - 14s 209ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 49s 740ms/step - loss: 3.6452 - box_loss: 1.9311 - class_loss: 1.7141 - val_loss: 4.1258 - val_box_loss: 2.3110 - val_class_loss: 1.8148 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 5.0000e-04 Epoch 29/500 61/61 [==============================] - 14s 209ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 49s 725ms/step - loss: 3.6211 - box_loss: 1.9318 - class_loss: 1.6893 - val_loss: 4.0777 - val_box_loss: 2.2792 - val_class_loss: 1.7986 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 5.0000e-04 Epoch 30/500 61/61 [==============================] - 14s 210ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 49s 728ms/step - loss: 3.5421 - box_loss: 1.9030 - class_loss: 1.6391 - val_loss: 4.1584 - val_box_loss: 2.2245 - val_class_loss: 1.9339 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 5.0000e-04 Epoch 31/500 61/61 [==============================] - 14s 209ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 49s 736ms/step - loss: 3.6820 - box_loss: 1.9344 - class_loss: 1.7476 - val_loss: 4.2140 - val_box_loss: 2.2910 - val_class_loss: 1.9230 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 5.0000e-04 Epoch 32/500 61/61 [==============================] - 14s 210ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 49s 733ms/step - loss: 3.4912 - box_loss: 1.8736 - class_loss: 1.6176 - val_loss: 4.1515 - val_box_loss: 2.2443 - val_class_loss: 1.9072 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 5.0000e-04 Epoch 33/500 61/61 [==============================] - 14s 210ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 49s 725ms/step - loss: 3.5931 - box_loss: 1.9194 - class_loss: 1.6736 - val_loss: 3.9628 - val_box_loss: 2.2946 - val_class_loss: 1.6682 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 5.0000e-04 Epoch 34/500 61/61 [==============================] - 14s 210ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 49s 729ms/step - loss: 3.3533 - box_loss: 1.8217 - class_loss: 1.5316 - val_loss: 4.1885 - val_box_loss: 2.4031 - val_class_loss: 1.7854 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 5.0000e-04 Epoch 35/500 61/61 [==============================] - 14s 210ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 49s 722ms/step - loss: 3.3616 - box_loss: 1.8237 - class_loss: 1.5379 - val_loss: 3.8710 - val_box_loss: 2.2622 - val_class_loss: 1.6088 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 5.0000e-04 Epoch 36/500 61/61 [==============================] - 14s 209ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 49s 729ms/step - loss: 3.4668 - box_loss: 1.8511 - class_loss: 1.6157 - val_loss: 4.0719 - val_box_loss: 2.2149 - val_class_loss: 1.8570 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 5.0000e-04 Epoch 37/500 61/61 [==============================] - 14s 210ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 49s 722ms/step - loss: 3.3800 - box_loss: 1.8525 - class_loss: 1.5275 - val_loss: 4.0870 - val_box_loss: 2.1881 - val_class_loss: 1.8989 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 5.0000e-04 Epoch 38/500 61/61 [==============================] - 14s 210ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 49s 739ms/step - loss: 3.3507 - box_loss: 1.7956 - class_loss: 1.5551 - val_loss: 3.9998 - val_box_loss: 2.2159 - val_class_loss: 1.7839 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 5.0000e-04 Epoch 39/500 61/61 [==============================] - 14s 209ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 49s 722ms/step - loss: 3.2639 - box_loss: 1.7946 - class_loss: 1.4693 - val_loss: 4.2740 - val_box_loss: 2.2606 - val_class_loss: 2.0134 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 5.0000e-04 Epoch 40/500 61/61 [==============================] - 14s 209ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 49s 734ms/step - loss: 3.4085 - box_loss: 1.8314 - class_loss: 1.5771 - val_loss: 3.7140 - val_box_loss: 2.1661 - val_class_loss: 1.5478 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 5.0000e-04 Epoch 41/500 61/61 [==============================] - 14s 209ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 49s 728ms/step - loss: 3.1781 - box_loss: 1.7495 - class_loss: 1.4286 - val_loss: 3.9894 - val_box_loss: 2.1937 - val_class_loss: 1.7957 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 5.0000e-04 Epoch 42/500 61/61 [==============================] - 14s 209ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 49s 731ms/step - loss: 3.2232 - box_loss: 1.7680 - class_loss: 1.4552 - val_loss: 4.0686 - val_box_loss: 2.2141 - val_class_loss: 1.8545 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 5.0000e-04 Epoch 43/500 61/61 [==============================] - 14s 209ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 49s 722ms/step - loss: 3.1036 - box_loss: 1.7011 - class_loss: 1.4025 - val_loss: 4.1366 - val_box_loss: 2.3250 - val_class_loss: 1.8116 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 5.0000e-04 Epoch 44/500 61/61 [==============================] - 14s 209ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 49s 729ms/step - loss: 3.2601 - box_loss: 1.7871 - class_loss: 1.4730 - val_loss: 4.0340 - val_box_loss: 2.1649 - val_class_loss: 1.8691 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 5.0000e-04 Epoch 45/500 61/61 [==============================] - 14s 209ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 49s 727ms/step - loss: 3.2063 - box_loss: 1.7550 - class_loss: 1.4513 - val_loss: 4.0398 - val_box_loss: 2.2455 - val_class_loss: 1.7942 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 5.0000e-04 Epoch 46/500 61/61 [==============================] - 14s 209ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 49s 729ms/step - loss: 3.1981 - box_loss: 1.7697 - class_loss: 1.4284 - val_loss: 3.8081 - val_box_loss: 2.0950 - val_class_loss: 1.7131 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 5.0000e-04 Epoch 47/500 61/61 [==============================] - 14s 209ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 49s 732ms/step - loss: 3.1242 - box_loss: 1.7338 - class_loss: 1.3904 - val_loss: 3.9232 - val_box_loss: 2.1795 - val_class_loss: 1.7437 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 5.0000e-04 Epoch 48/500 61/61 [==============================] - 14s 209ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 50s 748ms/step - loss: 3.1975 - box_loss: 1.7620 - class_loss: 1.4354 - val_loss: 4.0097 - val_box_loss: 2.1800 - val_class_loss: 1.8297 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 5.0000e-04 Epoch 49/500 61/61 [==============================] - 14s 209ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 49s 732ms/step - loss: 3.2196 - box_loss: 1.7738 - class_loss: 1.4458 - val_loss: 3.7698 - val_box_loss: 2.0886 - val_class_loss: 1.6811 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 5.0000e-04 Epoch 50/500 61/61 [==============================] - 14s 210ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 49s 738ms/step - loss: 3.1943 - box_loss: 1.7217 - class_loss: 1.4726 - val_loss: 3.7602 - val_box_loss: 2.2179 - val_class_loss: 1.5423 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 5.0000e-04 Epoch 51/500 61/61 [==============================] - 14s 209ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 49s 727ms/step - loss: 3.1599 - box_loss: 1.7671 - class_loss: 1.3928 - val_loss: 3.6969 - val_box_loss: 2.1322 - val_class_loss: 1.5647 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 5.0000e-04 Epoch 52/500 61/61 [==============================] - 14s 209ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 49s 727ms/step - loss: 3.1510 - box_loss: 1.7351 - class_loss: 1.4159 - val_loss: 3.7213 - val_box_loss: 2.2351 - val_class_loss: 1.4862 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 5.0000e-04 Epoch 53/500 61/61 [==============================] - 14s 209ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 49s 729ms/step - loss: 3.0229 - box_loss: 1.6645 - class_loss: 1.3585 - val_loss: 3.7538 - val_box_loss: 2.1224 - val_class_loss: 1.6315 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 5.0000e-04 Epoch 54/500 61/61 [==============================] - 14s 210ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 49s 728ms/step - loss: 3.0487 - box_loss: 1.7094 - class_loss: 1.3393 - val_loss: 3.8228 - val_box_loss: 2.1354 - val_class_loss: 1.6875 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 5.0000e-04 Epoch 55/500 61/61 [==============================] - 14s 209ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 49s 725ms/step - loss: 3.0313 - box_loss: 1.6890 - class_loss: 1.3423 - val_loss: 3.6646 - val_box_loss: 2.1374 - val_class_loss: 1.5272 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 5.0000e-04 Epoch 56/500 61/61 [==============================] - 14s 209ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Epoch 56: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628. 61/61 [==============================] - 49s 734ms/step - loss: 3.0084 - box_loss: 1.6724 - class_loss: 1.3359 - val_loss: 3.7237 - val_box_loss: 2.2127 - val_class_loss: 1.5110 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 5.0000e-04 Epoch 57/500 61/61 [==============================] - 14s 209ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 49s 729ms/step - loss: 2.9432 - box_loss: 1.6516 - class_loss: 1.2916 - val_loss: 3.5733 - val_box_loss: 2.0952 - val_class_loss: 1.4781 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 2.5000e-04 Epoch 58/500 61/61 [==============================] - 14s 209ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 49s 728ms/step - loss: 2.8623 - box_loss: 1.6233 - class_loss: 1.2390 - val_loss: 3.5008 - val_box_loss: 2.0765 - val_class_loss: 1.4243 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 2.5000e-04 Epoch 59/500 61/61 [==============================] - 14s 210ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 50s 739ms/step - loss: 3.0219 - box_loss: 1.6108 - class_loss: 1.4111 - val_loss: 3.6169 - val_box_loss: 2.0631 - val_class_loss: 1.5538 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 2.5000e-04 Epoch 60/500 61/61 [==============================] - 14s 209ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 49s 730ms/step - loss: 2.8969 - box_loss: 1.6240 - class_loss: 1.2729 - val_loss: 3.6932 - val_box_loss: 2.0761 - val_class_loss: 1.6171 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 2.5000e-04 Epoch 61/500 61/61 [==============================] - 14s 209ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 49s 727ms/step - loss: 2.8796 - box_loss: 1.6460 - class_loss: 1.2336 - val_loss: 3.5852 - val_box_loss: 2.0423 - val_class_loss: 1.5429 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 2.5000e-04 Epoch 62/500 61/61 [==============================] - 14s 209ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 49s 722ms/step - loss: 2.7833 - box_loss: 1.6135 - class_loss: 1.1698 - val_loss: 3.5711 - val_box_loss: 2.0761 - val_class_loss: 1.4949 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 2.5000e-04 Epoch 63/500 61/61 [==============================] - 14s 209ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 49s 735ms/step - loss: 2.7943 - box_loss: 1.5968 - class_loss: 1.1975 - val_loss: 3.5575 - val_box_loss: 2.0851 - val_class_loss: 1.4724 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 2.5000e-04 Epoch 64/500 61/61 [==============================] - 14s 210ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 49s 736ms/step - loss: 2.8447 - box_loss: 1.5967 - class_loss: 1.2480 - val_loss: 3.5775 - val_box_loss: 2.0603 - val_class_loss: 1.5173 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 2.5000e-04 Epoch 65/500 61/61 [==============================] - 14s 209ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 49s 732ms/step - loss: 2.8254 - box_loss: 1.6043 - class_loss: 1.2210 - val_loss: 3.4727 - val_box_loss: 2.0455 - val_class_loss: 1.4272 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 2.5000e-04 Epoch 66/500 61/61 [==============================] - 14s 210ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 49s 734ms/step - loss: 2.7964 - box_loss: 1.6230 - class_loss: 1.1734 - val_loss: 3.5492 - val_box_loss: 2.0259 - val_class_loss: 1.5233 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 2.5000e-04 Epoch 67/500 61/61 [==============================] - 14s 209ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 49s 735ms/step - loss: 2.7813 - box_loss: 1.5837 - class_loss: 1.1975 - val_loss: 3.5653 - val_box_loss: 2.0864 - val_class_loss: 1.4789 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 2.5000e-04 Epoch 68/500 61/61 [==============================] - 14s 210ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 49s 724ms/step - loss: 2.7174 - box_loss: 1.5441 - class_loss: 1.1733 - val_loss: 3.5764 - val_box_loss: 2.1241 - val_class_loss: 1.4522 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 2.5000e-04 Epoch 69/500 61/61 [==============================] - 14s 210ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 49s 731ms/step - loss: 2.7934 - box_loss: 1.5829 - class_loss: 1.2105 - val_loss: 3.3490 - val_box_loss: 1.9823 - val_class_loss: 1.3667 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 2.5000e-04 Epoch 70/500 61/61 [==============================] - 14s 209ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 49s 734ms/step - loss: 2.7303 - box_loss: 1.5593 - class_loss: 1.1710 - val_loss: 3.5377 - val_box_loss: 2.0445 - val_class_loss: 1.4932 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 2.5000e-04 Epoch 71/500 61/61 [==============================] - 14s 210ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 49s 730ms/step - loss: 2.8119 - box_loss: 1.6136 - class_loss: 1.1982 - val_loss: 3.4896 - val_box_loss: 2.0046 - val_class_loss: 1.4850 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 2.5000e-04 Epoch 72/500 61/61 [==============================] - 14s 209ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 49s 727ms/step - loss: 2.7313 - box_loss: 1.5856 - class_loss: 1.1457 - val_loss: 3.4694 - val_box_loss: 2.0046 - val_class_loss: 1.4648 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 2.5000e-04 Epoch 73/500 61/61 [==============================] - 14s 209ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 50s 736ms/step - loss: 2.7545 - box_loss: 1.5987 - class_loss: 1.1558 - val_loss: 3.5898 - val_box_loss: 2.0489 - val_class_loss: 1.5409 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 2.5000e-04 Epoch 74/500 61/61 [==============================] - 14s 210ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 49s 725ms/step - loss: 2.6912 - box_loss: 1.5414 - class_loss: 1.1498 - val_loss: 3.5309 - val_box_loss: 2.1282 - val_class_loss: 1.4027 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 2.5000e-04 Epoch 75/500 61/61 [==============================] - 14s 210ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 49s 732ms/step - loss: 2.7554 - box_loss: 1.5817 - class_loss: 1.1738 - val_loss: 3.6079 - val_box_loss: 2.1017 - val_class_loss: 1.5062 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 2.5000e-04 Epoch 76/500 61/61 [==============================] - 14s 210ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Epoch 76: ReduceLROnPlateau reducing learning rate to 0.0001250000059371814. 61/61 [==============================] - 49s 734ms/step - loss: 2.7209 - box_loss: 1.5752 - class_loss: 1.1457 - val_loss: 3.6318 - val_box_loss: 2.1236 - val_class_loss: 1.5082 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 2.5000e-04 Epoch 77/500 61/61 [==============================] - 14s 209ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 49s 720ms/step - loss: 2.6532 - box_loss: 1.5305 - class_loss: 1.1227 - val_loss: 3.3776 - val_box_loss: 2.0158 - val_class_loss: 1.3618 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 1.2500e-04 Epoch 78/500 61/61 [==============================] - 14s 209ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 50s 739ms/step - loss: 2.5906 - box_loss: 1.4967 - class_loss: 1.0939 - val_loss: 3.4985 - val_box_loss: 2.0258 - val_class_loss: 1.4728 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 1.2500e-04 Epoch 79/500 61/61 [==============================] - 14s 210ms/step creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.00s). Accumulating evaluation results... Please run evaluate() first DONE (t=0.00s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000 61/61 [==============================] - 49s 730ms/step - loss: 2.5620 - box_loss: 1.4829 - class_loss: 1.0792 - val_loss: 3.4960 - val_box_loss: 2.0688 - val_class_loss: 1.4272 - val_AP: -1.0000 - val_AP50: -1.0000 - val_AP75: -1.0000 - val_APs: -1.0000 - val_APm: -1.0000 - val_APl: -1.0000 - val_ARmax1: -1.0000 - val_ARmax10: -1.0000 - val_ARmax100: -1.0000 - val_ARs: -1.0000 - val_ARm: -1.0000 - val_ARl: -1.0000 - lr: 1.2500e-04
In [17]:
yolo.save('./model/trained_yolov8_xs.keras')
/home/lehoangchibach/anaconda3/envs/gpu11_cv/lib/python3.11/site-packages/keras_cv/models/task.py:43: UserWarning: `Model.state_updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically. return id(getattr(self, attr)) not in self._functional_layer_ids /home/lehoangchibach/anaconda3/envs/gpu11_cv/lib/python3.11/site-packages/keras_cv/models/task.py:43: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically. return id(getattr(self, attr)) not in self._functional_layer_ids
2-3 Model Evaluation¶
In [18]:
def plot_history(network_history, x):
plt.figure()
plt.xlabel("Epochs")
plt.ylabel("Box_loss")
plt.plot(network_history.history["box_loss"][x:])
plt.plot(network_history.history["val_box_loss"][x:])
plt.legend(["Training", "Validation"])
plot_history(network_history, 3)
In [19]:
## No augumentation for the test dataset
def process_test():
image_ids, annos = convert_annotations("val")
image_paths = []
boxes = []
class_ids = []
for i in image_ids:
x = annos[i]
image_paths.append(x["image_path"])
boxes.append(x["boxes"])
class_ids.append(x["class_ids"])
bbox = tf.ragged.constant(boxes)
classes = tf.ragged.constant(class_ids)
image_paths = tf.ragged.constant(image_paths)
data = tf.data.Dataset.from_tensor_slices((image_paths, classes, bbox))
ds = data.map(load_dataset, num_parallel_calls=tf.data.AUTOTUNE)
ds = ds.shuffle(BATCH_SIZE * 4)
ds = ds.ragged_batch(BATCH_SIZE, drop_remainder=True)
ds = ds.map(
keras_cv.layers.Resizing(
640, 640, bounding_box_format="xywh", pad_to_aspect_ratio=True
),
num_parallel_calls=tf.data.AUTOTUNE,
)
return ds
In [20]:
yolo.prediction_decoder = keras_cv.layers.NonMaxSuppression(
bounding_box_format="xywh",
from_logits=True,
iou_threshold=0.5,
confidence_threshold=0.75,
)
test_ds = process_test()
test_ds = test_ds.map(dict_to_tuple, num_parallel_calls=tf.data.AUTOTUNE)
test_ds = test_ds.prefetch(tf.data.AUTOTUNE)
result = yolo.evaluate(test_ds, return_dict=True)
print(result)
267/267 [==============================] - 33s 114ms/step - loss: 3.6321 - box_loss: 2.1626 - class_loss: 1.4696
{'loss': 3.6321470737457275, 'box_loss': 2.162588357925415, 'class_loss': 1.4695600271224976}
2-4 Prediction¶
In [21]:
# Reference: https://keras.io/guides/keras_cv/object_detection_keras_cv/#training-our-model example
image, bbox = next(iter(test_ds))
y_pred = yolo.predict(image)
y_pred = bounding_box.to_ragged(y_pred)
visualization.plot_bounding_box_gallery(
image,
value_range=(0, 255),
bounding_box_format="xywh",
y_true=bbox,
y_pred=y_pred,
scale=4,
rows=2,
cols=2,
show=True,
font_scale=0.7,
class_mapping=class_mapping,
)
1/1 [==============================] - 2s 2s/step
In [22]:
gc.collect()
K.clear_session()